Quantitative [18]FDG PET asymmetry features predict long-term seizure recurrence in refractory epilepsy

Abstract

Objective: Fluorodeoxyglucose-positron emission tomography (FDG-PET) is an established, independent, strong predictor of surgical outcome in refractory epilepsy. In this study, we explored the added value of quantitative [ 18 F]FDG-PET features combined with clinical variables, including electroencephalography (EEG), [ 18 F]FDG-PET, and magnetic resonance imaging (MRI) qualitative interpretations, to predict long-term seizure recurrence (mean post-op follow-up of 5.85 ± 3.77 years). Methods: Machine learning predictive models of surgical outcome were created using a random forest classifier trained on quantitative features in 89 patients with drug-refractory temporal lobe epilepsy evaluated at the Hospital of the University of Pennsylvania epilepsy surgery program (2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016). Quantitative features were calculated from asymmetry features derived from image processing using Advanced Normalization Tools (ANTs). Results:The best-performing model used quantification and had an out-of-bag accuracy of 0.71 in identifying patients with seizure recurrence (Engel IB or worse) which outperformed that using qualitative clinical data by 10%. This model is shared through open-source software for research use. In addition, several asymmetry features in temporal and extratemporal regions that were significantly associated with seizure freedom are identified for future study. Significance: Complex quantitative [ 18 F]FDG-PET imaging features can predict seizure recurrence in patients with refractory temporal lobe epilepsy. These initial retrospective results in a cohort with long-term follow-up suggest that using quantitative imaging features from regions in the epileptogenic network can inform the clinical decision-making process.

Volumetric glutamate imaging (GluCEST) using 7T MRI can lateralize nonlesional temporal lobe epilepsy: A preliminary study

“…However, PET is not always universally pursued in a pre-surgical workup, so the mix of PET findings is likely consistent with clinical presentations. Ongoing work by our lab seeks to incorporate a multi-modality imaging biomarker approach to pre-surgical epilepsy decision-making, in which PET and eventually GluCEST will play important roles (Kini et al, 2021). LGK and SD processed the data.…”

Enhancement of 18F-Fluorodeoxyglucose PET Image Quality by Deep-Learning-Based Image Reconstruction Using Advanced Intelligent Clear-IQ Engine in Semiconductor-Based PET/CT Scanners

“…It helps differentiate between benign and malignant lesions, determine the stage of cancers, and plan treatment methods [ 1 , 2 , 3 , 4 , 5 , 6 ]. It is used to diagnose ischemic, inflammatory, and degenerative diseases [ 7 , 8 , 9 , 10 , 11 , 12 ]. However, one of the main disadvantages of 18 F-FDG PET/CT is its low resolution.…”

Clinical Application of Machine Learning Models for Brain Imaging in Epilepsy: A Review

“…While most studies have used a region-ofinterest (ROI) to extract imaging features, the choice of atlases for ROIs varies. For example, some investigations used traditional automated anatomical labeling (AAL) (Fallahi et al, 2020;Si et al, 2020;Kini et al, 2021), and a different atlas was used in other studies (Gleichgerrcht et al, 2018(Gleichgerrcht et al, , 2020. Zhang et al ( , 2021 used radiomics as a novel method to extract imaging data, and this might provide greater usefulness than conventional methods (Gillies et al, 2016).…”

“…There are also various ML applications for more direct associations with clinical outcomes than lesion/focus detection (Table 4). A major trend in this section is the prediction of postsurgical seizure freedom (Bernhardt et al, 2015;Memarian et al, 2015;Munsell et al, 2015;He et al, 2017;Gleichgerrcht et al, 2018;Taylor et al, 2018;Gleichgerrcht et al, 2020;Larivière et al, 2020;Kini et al, 2021;Sinha et al, 2021), in light of the clinical importance. Most of the studies reported 70-90% accuracy for the prediction of seizure outcomes after resection surgery.…”